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README.md

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+# License Plate Recognition System
+
+This project is a License Plate Recognition system that utilizes a combination of computer vision and machine learning techniques to identify and process license plates from video footage.
+
+## Project Structure
+
+The project is organized as follows:
+
+- `main.py`: The main script that provides a GUI for uploading videos, processing them, and exporting the results.
+- `models/`: Contains the pre-trained models for license plate detection and object detection.
+    - `license_plate_detector.pt`: The model for detecting license plates.
+    - `yolov8n.pt`: The YOLO model for object detection.
+- `sort/`: Implements the SORT algorithm for object tracking.
+    - `sort.py`: The main SORT algorithm implementation.
+    - `data/`: Training data for the SORT algorithm.
+- `src/`: Contains the core functionality for processing videos and license plates.
+    - `get_plates.py`: Functions for extracting license plate data.
+    - `interpolate.py`: Interpolation utilities for smoothing bounding box coordinates.
+    - `process.py`: Core processing functions for video and license plate recognition.
+    - `util.py`: Utility functions.
+    - `visualize.py`: Functions for visualizing the results.
+
+## Setup
+
+To set up the project, follow these steps:
+
+1. Ensure Python 3.8 or higher is installed.
+2. Install the required Python packages by running `pip install -r requirements.txt`.
+3. Download the pre-trained models and place them in the `models/` directory.
+
+## Usage
+
+To use the system, run `main.py` and follow the GUI prompts:
+
+1. Upload a video file.
+2. Click "Process Video" to start the license plate recognition process.
+3. Export the results to an Excel file or visualize the processed video.
+
+## Dependencies
+
+- filterpy
+- scikit-image
+- pandas
+- ultralytics
+- easyocr
+- scipy
+- lap
+- opencv-python
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.